Your eval passed. The hallucination shipped anyway.
variA/Bly's evaluation engine sees what your current scorer misses - retrieved context, claim-level grounding, multi-turn consistency, drift over time. Six categories, 40+ metrics. The full picture before you ship.
Every claim verified against its source.
Score responses against retrieved context, not just the prompt. Catch hallucinations that blind evaluation misses.
Claim-Level Verification
Trace every factual claim back to retrieved chunks. Ungrounded claims flagged with the exact sentence that hallucinated.
- Source document tracing
- Sentence-level flagging
- Grounding confidence scores
Faithfulness Scoring
Measure how accurately responses reflect the provided context. Detect subtle distortions and unsupported inferences.
- Context alignment
- Inference validation
- Distortion detection
Context Utilization
Understand how effectively your AI uses retrieved information. Identify unused context and missed relevant chunks.
- Chunk utilization rate
- Relevance scoring
- Coverage analysis
Track consistency across every turn.
Detect contradictions, context loss, and coreference failures across multi-turn conversations.
Multi-Turn Consistency
Automatically detect when turn 5 contradicts turn 2. Track semantic alignment across the full conversation.
- Contradiction detection
- Semantic alignment
- Turn-pair analysis
Context Retention
Measure how well your AI retains information provided earlier in the conversation. Catch context amnesia.
- Retention scoring
- Information decay tracking
- Memory benchmarks
Progressive Depth
Evaluate whether your AI builds on previous turns effectively. Detect circular responses and stalled conversations.
- Depth progression
- Circular response detection
- Topic evolution
Test changes on real traffic.
Run controlled experiments on prompts, models, and retrieval strategies using production traffic. Measure quality, cost, and latency simultaneously.
Catch degradation before users do.
Monitor quality scores over time and get alerted when AI behavior shifts.
Quality Drift Monitoring
Track evaluation scores over time across 40+ metrics. Detect gradual degradation and sudden drops.
- Time-series tracking
- Anomaly detection
- Dimension-level drill-down
Regression Alerts
Get notified when any critical dimension degrades beyond your configured threshold. Stop issues before they reach users.
- Configurable thresholds
- Multi-channel alerts
- Severity classification
Historical Comparison
Compare current performance against any historical baseline. Understand exactly when and why quality changed.
- Baseline snapshots
- Delta analysis
- Root cause identification
Understand the business impact of every change.
Measure cost, latency, and quality together. Ship the variant that optimizes your priorities.
Cost vs Quality vs Time Tradeoff
Compare quality improvements against cost and iteration time. Know exactly what each quality point costs.
Latency Impact
Measure end-to-end response time impact of every change. Balance user experience with quality.
Token Efficiency
Track token usage per variant and per model. Optimize prompts for cost without sacrificing quality.
From observation to deployment
Five steps to context-aware AI improvement.
Observe
Log production traffic with full context - chunks, history, instructions.
Evaluate
Multi-dimensional scoring with 40+ metrics and grounding verification.
Experiment
A/B test changes on real production traffic.
Decide
Statistical significance meets behavioral insight.
Ship
Deploy the winner and monitor for drift.
Multi-Provider Support
OpenAI, Anthropic, Google, Cohere, and more. Evaluate across models without changing your workflow.
Developer Integration
Production-ready SDKs and APIs. Setup in under 10 minutes.
Enterprise Security
End-to-end encryption, SSO/SAML, and role-based access control.
Ready to ship AI you can defend?
Deterministic evaluation gives you the audit trail behind every response. Start with 1,000 free credits.
1,000 free credits.